AI/Autonomous Systems Engineer

Location: Lusail, Qatar

Department: Tech

• Develop and implement autonomous perception, localisation, and navigation algorithms for ground robots and aerial drones within Snoonu’s AI-driven autonomous logistics platform.
• Design and optimize multi-sensor perception pipelines using LiDAR, radar, RGB/depth cameras, IMU, and GPS to ensure robust operation in Qatar’s challenging conditions (heat, dust, glare, reflective surfaces).
• Develop and improve object detection, free-space segmentation, tracking, and obstacle prediction modules to support safe real-world navigation.
• Implement localisation and mapping capabilities, including Visual-Inertial SLAM, sensor fusion-based localization, and GPS-assisted fallback mechanisms.
• Build simulation-based testing workflows to validate autonomy performance under realistic Qatari environments, traffic patterns, and operational constraints.
• Collaborate with robotics, embedded, and cloud teams to ensure seamless integration of perception outputs into motion planning, control, fleet management, and RaaS orchestration layers.
• Evaluate model performance using defined validation metrics (accuracy, latency, robustness), and contribute to iterative improvements to achieve TRL advancement targets.
• Support dataset collection, labeling strategies, and pipeline improvements to enable continuous model training and validation from real-world deployments.
• Participate in on-site experiments, field testing, and pilot demonstrations, providing debugging and performance tuning of autonomy modules.
• Document model designs, experimental results, and validation procedures to support R&D reporting, regulatory compliance, and knowledge transfer activities.

• Bachelor’s or Master’s degree in Artificial Intelligence, Robotics, Computer Vision, Computer Engineering, or a related field. (PhD is a plus for research-heavy autonomy work.)
• 2–4 years of backend development experience, with strong hands-on Python/C++ expertise.
• Strong analytical and problem-solving skills with the ability to work on complex real-world robotics challenges.
• Research-oriented mindset and ability to translate experimentation into production-ready autonomy improvements.
• Ability to work effectively in cross-functional teams (robotics, embedded, platform/software, operations).
• Clear communication skills and ability to document technical work, trade-offs, and validation outcomes.
• High ownership and accountability for results, timelines, and engineering quality.
• Adaptability to fast-paced R&D environments involving prototyping, testing, and iterative development.
• Strong business context understanding, able to translate operational needs into technical solutions.
• Open to feedback and proactive in applying improvements suggested by senior engineers or tech leads.

• Strong foundation in Python, including OOP principles, design patterns, and writing clean, maintainable code.
• Experience building backend services using frameworks such as FastAPI, Flask, or Django.
• Ability to design, develop, and maintain RESTful APIs with proper error handling and logging.

• Experience using AWS services, such as Lambda, SQS/SNS, API Gateway, Step Functions, DynamoDB, RDS, S3, and CloudWatch.
• Experience designing event-driven and serverless architectures.
• Familiarity with IAM, environment configuration, and cloud security best practices.

• Strong proficiency in Python and experience using deep learning frameworks
• Experience in one or more autonomy domains:
  • Computer Vision and Perception Pipelines
  • Sensor fusion
  • Localisation and mapping (SLAM / Visual-Inertial Odometry)
  • Object detection / tracking / segmentation
  • Motion planning support systems
• Familiarity with robotics development environments such as ROS / ROS2 and real-time robotics data pipelines.
• Experience working with LiDAR point clouds, camera streams, and IMU-based motion data.
• Ability to optimize ML/AI systems for real-time performance and edge deployment constraints.
• Experience with simulation tools or autonomy test environments is a strong plus (e.g., Gazebo, CARLA, AirSim, Isaac Sim).
• Knowledge of safe autonomy principles, anomaly detection, or human-in-the-loop safety mechanisms is a plus.